MMDetection API#

model_hub.mmdetection#

class model_hub.mmdetection.MMDetTrial(context: determined.pytorch._pytorch_context.PyTorchTrialContext)#

This trial serves as the trainer for MMDetection models. It replaces the mmcv runner used by MMDetection.

For nearly all use cases, you can just use this trial definition and control behavior by changing the MMDetection config. If you want to customize the trial further, you can use this trial as the starting point.

Simlar to using the MMDetection library directly, the main way users customize an experiment is by modifying the MMDetection config. To find out how to configure MMDetection using the experiment configuration file, visit the readme.

Helper Functions#

class model_hub.mmdetection.GCSBackend(*args: Any, **kwargs: Any)#

To use a Google Storage bucket as the storage backend, set data.file_client_args field of the experiment config as follows:

data:
  file_client_args:
    backend: gcs
    bucket_name: <FILL IN>
get_local_path(filepath: str) → Iterator[str]#

Download a file from filepath. get_local_path is decorated by contxtlib.contextmanager(). It can be called with with statement, and when exists from the with statement, the temporary path will be released. :param filepath: Download a file from filepath. :type filepath: str

class model_hub.mmdetection.S3Backend(*args: Any, **kwargs: Any)#

To use a S3 bucket as the storage backend, set data.file_client_args field of the experiment config as follows:

data:
  file_client_args:
    backend: s3
    bucket_name: <FILL IN>
get_local_path(filepath: str) → Iterator[str]#

Download a file from filepath. get_local_path is decorated by contxtlib.contextmanager(). It can be called with with statement, and when exists from the with statement, the temporary path will be released. :param filepath: Download a file from filepath. :type filepath: str

model_hub.mmdetection.get_pretrained_ckpt_path(download_directory: str, config_file: str) → Tuple[Any, Any]#

If the config_file has an associated pretrained checkpoint, return path to downloaded checkpoint and preloaded checkpoint

Parameters

  • download_directory – path to download checkpoints to

  • config_file – mmdet config file path for which to find and load pretrained weights

Returns

checkpoint path, loaded checkpoint